{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "%matplotlib inline\n",
    "\n",
    "def make_fake_data_point():\n",
    "    seed = random.randint(0,10)\n",
    "    if seed < 2:\n",
    "        return random.normalvariate(170, 40)\n",
    "    if seed < 4:\n",
    "        return random.normalvariate(80, 20)\n",
    "    if seed < 5:\n",
    "        return random.normalvariate(120, 10)\n",
    "    if seed < 8:\n",
    "        return random.normalvariate(320, 15)\n",
    "    if seed < 9:\n",
    "        return random.normalvariate(210, 10)\n",
    "    return random.normalvariate(30, 5)\n",
    "\n",
    "def create_fake_data(number_of_points):\n",
    "    return pd.DataFrame([int(make_fake_data_point()) for i in range(number_of_points)], columns=[\"weight\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight\n",
       "0      69\n",
       "1     200\n",
       "2      79\n",
       "3      27\n",
       "4      41"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = create_fake_data(1000)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10905b2e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m8f+ecev3+/2Rx3v3u5fGnt9DuohBZ+18e/ZcxZkzpxv/9zc57vV6rcqz0XhVW/KMz9dj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\nB/bCuUsicLV1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10902e748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['weight'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-means\n",
    "\n",
    "One among many clustering algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "km = KMeans(n_clusters=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight\n",
       "0      69\n",
       "1     200\n",
       "2      79\n",
       "3      27\n",
       "4      41"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['weight']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=6, n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.fit(df[['weight']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['prediction'] = km.predict(df[['weight']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prediction\n",
       "0    Axes(0.125,0.125;0.775x0.775)\n",
       "1    Axes(0.125,0.125;0.775x0.775)\n",
       "2    Axes(0.125,0.125;0.775x0.775)\n",
       "3    Axes(0.125,0.125;0.775x0.775)\n",
       "4    Axes(0.125,0.125;0.775x0.775)\n",
       "5    Axes(0.125,0.125;0.775x0.775)\n",
       "Name: weight, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2mJndFi3fYWaHzWzZzB4wswuyjysiInHEeed+Gvhdd/854F8BHzGztwH7ga+5+1uBB4E7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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc8e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('prediction')['weight'].hist(bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Team</th>\n",
       "      <th>POS</th>\n",
       "      <th>Number</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Years</th>\n",
       "      <th>1st Year</th>\n",
       "      <th>DOB</th>\n",
       "      <th>School</th>\n",
       "      <th>City</th>\n",
       "      <th>State/Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Race</th>\n",
       "      <th>HS Only</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gee, Alonzo</td>\n",
       "      <td>26</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>F</td>\n",
       "      <td>33</td>\n",
       "      <td>3250000.0</td>\n",
       "      <td>78</td>\n",
       "      <td>219</td>\n",
       "      <td>4</td>\n",
       "      <td>2009</td>\n",
       "      <td>5/29/1987</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Riviera Beach, FL</td>\n",
       "      <td>Florida</td>\n",
       "      <td>US</td>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wallace, Gerald</td>\n",
       "      <td>31</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>10105855.0</td>\n",
       "      <td>79</td>\n",
       "      <td>220</td>\n",
       "      <td>12</td>\n",
       "      <td>2001</td>\n",
       "      <td>7/23/1982</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Sylacauga, AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Williams, Mo</td>\n",
       "      <td>30</td>\n",
       "      <td>Trail Blazers</td>\n",
       "      <td>G</td>\n",
       "      <td>25</td>\n",
       "      <td>2652000.0</td>\n",
       "      <td>73</td>\n",
       "      <td>195</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>12/19/1982</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jackson, MS</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>US</td>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gladness, Mickell</td>\n",
       "      <td>27</td>\n",
       "      <td>Magic</td>\n",
       "      <td>C</td>\n",
       "      <td>40</td>\n",
       "      <td>762195.0</td>\n",
       "      <td>83</td>\n",
       "      <td>220</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>7/26/1986</td>\n",
       "      <td>Alabama A&amp;M</td>\n",
       "      <td>Birmingham, AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jefferson, Richard</td>\n",
       "      <td>33</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>F</td>\n",
       "      <td>44</td>\n",
       "      <td>11046000.0</td>\n",
       "      <td>79</td>\n",
       "      <td>230</td>\n",
       "      <td>12</td>\n",
       "      <td>2001</td>\n",
       "      <td>6/21/1980</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>California</td>\n",
       "      <td>US</td>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name  Age           Team POS Number      Salary  Height  \\\n",
       "0         Gee, Alonzo   26      Cavaliers   F     33   3250000.0      78   \n",
       "1     Wallace, Gerald   31        Celtics   F     45  10105855.0      79   \n",
       "2        Williams, Mo   30  Trail Blazers   G     25   2652000.0      73   \n",
       "3   Gladness, Mickell   27          Magic   C     40    762195.0      83   \n",
       "4  Jefferson, Richard   33           Jazz   F     44  11046000.0      79   \n",
       "\n",
       "   Weight  Years  1st Year         DOB       School               City  \\\n",
       "0     219      4      2009   5/29/1987      Alabama  Riviera Beach, FL   \n",
       "1     220     12      2001   7/23/1982      Alabama      Sylacauga, AL   \n",
       "2     195     10      2003  12/19/1982      Alabama        Jackson, MS   \n",
       "3     220      2      2011   7/26/1986  Alabama A&M     Birmingham, AL   \n",
       "4     230     12      2001   6/21/1980      Arizona    Los Angeles, CA   \n",
       "\n",
       "  State/Province Country   Race HS Only  \n",
       "0        Florida      US  Black      No  \n",
       "1        Alabama      US  Black      No  \n",
       "2    Mississippi      US  Black      No  \n",
       "3        Alabama      US  Black      No  \n",
       "4     California      US  Black      No  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"nba_2013_cleaned.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[(df['POS'] == 'C') | (df['POS'] == 'F') | (df['POS'] == 'G')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G    175\n",
       "F    142\n",
       "C     67\n",
       "Name: POS, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['POS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POS</th>\n",
       "      <th>POS_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  POS  POS_label\n",
       "0   F          1\n",
       "1   F          1\n",
       "2   G          2\n",
       "3   C          0\n",
       "4   F          1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "df['POS_label'] = le.fit_transform(df['POS'])\n",
    "df[['POS','POS_label']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10bf15198>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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QIgXJBVUhhBBjSHIXQogUJJ1lYl7qOXSIQH8/eQsXklFy+oJnLBKhc/dutGGQ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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd67390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Weight'], df['Height'], edgecolor='none', c=df['POS_label'], alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10bfc5278>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ypCWlpkYXEJlxKoDIFYhSUvjvrv5aq5HPO3XiHb63UpJXuOQt5lH//GbRKyzF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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be64da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Weight'], df['Height'], edgecolor='none', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Order of features is important! keep the same between fit and predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>219</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>220</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>195</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>220</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  Height\n",
       "0     219      78\n",
       "1     220      79\n",
       "2     195      73\n",
       "3     220      83\n",
       "4     230      79"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Weight', 'Height']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height  Weight\n",
       "0      78     219\n",
       "1      79     220\n",
       "2      73     195\n",
       "3      83     220\n",
       "4      79     230"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Height', 'Weight']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=4, n_init=10,\n",
       "    n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=4)\n",
    "km.fit(df[['Weight', 'Height']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Team</th>\n",
       "      <th>POS</th>\n",
       "      <th>Number</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Years</th>\n",
       "      <th>1st Year</th>\n",
       "      <th>DOB</th>\n",
       "      <th>School</th>\n",
       "      <th>City</th>\n",
       "      <th>State/Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Race</th>\n",
       "      <th>HS Only</th>\n",
       "      <th>POS_label</th>\n",
       "      <th>cluster_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gee, Alonzo</td>\n",
       "      <td>26</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>F</td>\n",
       "      <td>33</td>\n",
       "      <td>3250000.0</td>\n",
       "      <td>78</td>\n",
       "      <td>219</td>\n",
       "      <td>4</td>\n",
       "      <td>2009</td>\n",
       "      <td>5/29/1987</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Riviera Beach, FL</td>\n",
       "      <td>Florida</td>\n",
       "      <td>US</td>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wallace, Gerald</td>\n",
       "      <td>31</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>10105855.0</td>\n",
       "      <td>79</td>\n",
       "      <td>220</td>\n",
       "      <td>12</td>\n",
       "      <td>2001</td>\n",
       "      <td>7/23/1982</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Sylacauga, AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>US</td>\n",
       "      <td>Black</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name  Age       Team POS Number      Salary  Height  Weight  \\\n",
       "0      Gee, Alonzo   26  Cavaliers   F     33   3250000.0      78     219   \n",
       "1  Wallace, Gerald   31    Celtics   F     45  10105855.0      79     220   \n",
       "\n",
       "   Years  1st Year        DOB   School               City State/Province  \\\n",
       "0      4      2009  5/29/1987  Alabama  Riviera Beach, FL        Florida   \n",
       "1     12      2001  7/23/1982  Alabama      Sylacauga, AL        Alabama   \n",
       "\n",
       "  Country   Race HS Only  POS_label  cluster_4  \n",
       "0      US  Black      No          1          0  \n",
       "1      US  Black      No          1          0  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster_4'] = km.predict(df[['Weight', 'Height']])\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's in each cluster? Do they seem like they were categorized somewhat correctly?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster_4  POS\n",
       "0          F      104\n",
       "           G       30\n",
       "           C       16\n",
       "1          G      144\n",
       "           F        9\n",
       "           C        1\n",
       "2          C       50\n",
       "           F       29\n",
       "3          G        1\n",
       "Name: POS, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cluster_4')['POS'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not really... That's a lot of Guard/Forward/Centers together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hjHZDSjdvoXbsKGf37kpmzBjO8OHpLR7TWrN3bzXhcIw1a/Zz7FiQoUN9nHbaUGbMGM6+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\n/wfk2jHxL7OyRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cca6748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.scatter(df['Weight'], df['Height'], edgecolor='none', c=df['cluster_4'], alpha=0.5)\n",
    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:,1], s=100, c=np.unique(km.labels_))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oh no, k-means assumes all features are the same!! - 50 lb of weight is the same as 50\" of height\n",
    "\n",
    "Let's visualize that on a chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 350)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lZlZqZscAxwHLcw0nIiJDK5tDNu8Cfgccb2Z/NLOrgO8A55vZK8C58XncvQm4B2gCHgSu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\ngGjoi4gUkP8P3Zzj4lNuz1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce953c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.scatter(df['Weight'], df['Height'], edgecolor='none', c=df['cluster_4'], alpha=0.5)\n",
    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:,1], s=100, c=np.unique(km.labels_))\n",
    "plt.grid()\n",
    "plt.xlim([0,350])\n",
    "plt.ylim([0,350])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d1b3a20>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d1bdac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Weight'].hist()\n",
    "df['Height'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soma/.virtualenvs/data/lib/python3.5/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.17151614,  0.09952171, -1.52670541,  1.18367313,  0.09952171,\n",
       "        0.09952171,  0.09952171,  0.37055957, -0.9846297 , -1.25566756,\n",
       "       -0.9846297 ,  0.91263527, -0.442554  , -1.52670541, -1.52670541,\n",
       "       -1.52670541,  0.64159742,  0.09952171,  1.18367313, -0.9846297 ,\n",
       "       -0.9846297 ,  1.45471098,  0.64159742, -1.25566756, -0.9846297 ,\n",
       "        0.91263527, -0.17151614,  0.37055957, -0.442554  ,  1.45471098,\n",
       "        0.64159742,  0.09952171, -0.442554  ,  0.37055957, -1.25566756,\n",
       "        0.91263527,  0.37055957, -0.17151614, -0.9846297 , -0.9846297 ,\n",
       "        2.26782454,  0.91263527,  0.09952171,  0.09952171,  1.18367313,\n",
       "        0.37055957, -1.52670541, -0.442554  , -1.25566756,  0.37055957,\n",
       "       -0.9846297 ,  0.64159742, -1.52670541,  0.37055957, -0.9846297 ,\n",
       "       -0.9846297 ,  1.72574884, -0.9846297 ,  0.64159742,  0.64159742,\n",
       "        0.37055957,  1.18367313,  0.37055957, -0.17151614, -0.71359185,\n",
       "       -0.71359185, -1.25566756, -1.25566756, -0.442554  ,  1.18367313,\n",
       "        0.91263527,  0.91263527,  1.18367313,  0.64159742, -0.17151614,\n",
       "       -0.442554  ,  0.91263527,  0.37055957, -1.25566756,  0.64159742,\n",
       "        0.64159742,  0.91263527,  1.99678669,  0.64159742,  0.37055957,\n",
       "       -0.442554  ,  0.37055957,  0.64159742, -0.9846297 , -0.442554  ,\n",
       "       -1.79774327,  1.18367313,  0.37055957,  0.91263527,  1.45471098,\n",
       "        1.18367313, -0.9846297 ,  1.18367313,  1.72574884,  0.64159742,\n",
       "       -0.9846297 ,  0.64159742, -0.71359185, -0.71359185, -1.52670541,\n",
       "        0.37055957,  0.37055957, -0.71359185, -1.25566756, -0.17151614,\n",
       "        1.18367313,  1.45471098,  0.09952171,  0.64159742,  0.64159742,\n",
       "        0.91263527,  0.64159742,  0.91263527, -0.71359185, -1.25566756,\n",
       "       -0.442554  , -0.9846297 ,  0.91263527, -0.71359185,  1.18367313,\n",
       "        0.91263527,  1.18367313,  0.91263527,  0.09952171,  0.64159742,\n",
       "        0.37055957,  0.64159742, -0.9846297 , -1.25566756, -1.52670541,\n",
       "       -0.71359185, -0.71359185, -1.52670541, -0.442554  , -0.71359185,\n",
       "       -0.9846297 , -0.71359185,  0.37055957,  1.18367313,  0.91263527,\n",
       "       -1.25566756, -1.79774327, -0.17151614,  1.45471098,  0.37055957,\n",
       "       -0.71359185, -0.17151614, -0.17151614,  0.91263527, -0.71359185,\n",
       "       -0.71359185, -0.442554  , -1.25566756, -0.9846297 ,  1.72574884,\n",
       "       -0.9846297 , -0.17151614,  0.37055957,  0.64159742, -0.9846297 ,\n",
       "       -0.442554  , -0.17151614, -0.17151614,  0.91263527,  0.37055957,\n",
       "       -2.06878112, -0.71359185, -0.442554  , -1.52670541, -0.17151614,\n",
       "       -0.442554  ,  0.64159742,  0.09952171, -0.71359185,  0.09952171,\n",
       "        0.64159742,  0.91263527,  0.91263527, -1.25566756,  0.37055957,\n",
       "       -2.06878112,  0.37055957, -1.79774327, -0.71359185,  0.64159742,\n",
       "       -0.9846297 , -0.17151614, -1.25566756,  0.64159742,  1.18367313,\n",
       "        1.18367313,  1.72574884,  1.18367313, -0.17151614, -0.442554  ,\n",
       "        0.64159742,  1.45471098, -1.52670541,  0.64159742,  0.91263527,\n",
       "        0.37055957, -0.442554  ,  1.18367313,  1.18367313,  1.45471098,\n",
       "        1.72574884,  0.64159742, -0.17151614,  1.45471098, -0.9846297 ,\n",
       "        1.72574884,  0.64159742, -0.9846297 , -0.71359185,  0.91263527,\n",
       "        1.18367313,  1.45471098,  0.91263527,  0.91263527,  1.18367313,\n",
       "        1.18367313,  1.72574884, -0.9846297 , -0.9846297 ,  1.45471098,\n",
       "       -0.9846297 ,  0.09952171,  0.37055957,  0.91263527,  1.45471098,\n",
       "        1.18367313,  0.64159742,  0.91263527,  0.64159742,  0.37055957,\n",
       "       -1.52670541, -0.71359185, -2.06878112, -0.71359185,  0.64159742,\n",
       "        0.64159742,  0.37055957, -0.442554  ,  0.37055957, -1.79774327,\n",
       "       -0.442554  ,  1.45471098,  0.64159742, -1.52670541,  1.45471098,\n",
       "        1.45471098,  0.64159742, -1.52670541,  0.37055957,  0.91263527,\n",
       "       -0.17151614, -2.06878112, -0.442554  , -0.17151614, -1.25566756,\n",
       "       -1.79774327, -0.71359185,  0.09952171,  1.45471098,  1.45471098,\n",
       "        0.09952171,  0.64159742, -0.71359185, -0.17151614,  1.45471098,\n",
       "       -1.79774327, -1.79774327, -1.79774327, -0.442554  , -0.9846297 ,\n",
       "        1.18367313,  0.37055957, -0.71359185, -0.71359185, -1.52670541,\n",
       "        0.64159742,  0.09952171,  1.45471098,  1.45471098,  0.64159742,\n",
       "       -0.71359185, -0.17151614,  0.64159742,  0.64159742,  0.09952171,\n",
       "        0.64159742,  1.45471098, -0.71359185,  0.64159742,  0.37055957,\n",
       "        1.45471098,  1.45471098,  0.37055957,  0.64159742,  1.18367313,\n",
       "        1.45471098,  0.09952171,  0.37055957,  0.37055957, -0.17151614,\n",
       "       -0.71359185, -0.71359185, -0.442554  ,  0.64159742, -1.25566756,\n",
       "        0.64159742, -0.9846297 ,  0.64159742, -0.9846297 ,  1.18367313,\n",
       "        1.18367313, -0.17151614,  0.64159742, -1.25566756, -0.71359185,\n",
       "       -1.79774327,  1.18367313,  0.37055957, -0.9846297 , -0.17151614,\n",
       "       -0.71359185, -0.442554  ,  0.37055957,  1.18367313,  1.45471098,\n",
       "        0.09952171, -1.79774327, -1.25566756, -1.25566756, -0.71359185,\n",
       "       -0.442554  , -0.9846297 , -1.52670541,  0.64159742,  0.37055957,\n",
       "        0.64159742,  0.09952171,  1.45471098, -1.25566756, -1.25566756,\n",
       "        1.18367313, -0.71359185,  0.37055957, -1.52670541, -0.71359185,\n",
       "       -1.79774327,  0.37055957, -0.442554  , -0.71359185, -0.9846297 ,\n",
       "        0.64159742,  0.64159742, -1.79774327, -1.25566756, -1.79774327,\n",
       "        1.72574884, -0.17151614, -0.9846297 , -2.61085683, -2.61085683,\n",
       "       -0.17151614, -0.9846297 ,  0.37055957,  1.18367313,  0.64159742,\n",
       "        0.09952171,  0.64159742, -0.9846297 ,  1.18367313,  0.91263527,\n",
       "        0.09952171, -0.9846297 ,  0.64159742, -0.71359185])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.scale(df['Height'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soma/.virtualenvs/data/lib/python3.5/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/Users/soma/.virtualenvs/data/lib/python3.5/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "df['scaled_height'] = preprocessing.scale(df['Height'])\n",
    "df['scaled_weight'] = preprocessing.scale(df['Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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VwuEEc+dWUVlZyJo1+9m8uZ6WligjRwa46KJJvP12HVu2NJBMapYsmciECaXcd9/bHG5q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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d1c15f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.scatter(df['scaled_weight'], df['scaled_height'], edgecolor='none', c=df['cluster_4'], alpha=0.5)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make a new KMeans, 3 clusters\n",
    "# fit it with the scaled weight and height\n",
    "# predict it based on scaled weight and height\n",
    "# store those labels into a new column\n",
    "# graph it with the new labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    110\n",
       "4     99\n",
       "0     92\n",
       "1     82\n",
       "3      1\n",
       "Name: scaled_prediction, dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=5)\n",
    "km.fit(df[['scaled_weight', 'scaled_height']])\n",
    "df['scaled_prediction'] = km.predict(df[['scaled_weight', 'scaled_height']])\n",
    "df['scaled_prediction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4/fbu87KbvezlnfK/kTM/h1jiOMABHMSwgXXUUkuQAHvYjRMnAQIoFA4c7GUv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c882f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['scaled_weight'], df['scaled_height'], edgecolor='none', c=df['scaled_prediction'], alpha=0.5)\n",
    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:,1], s=100, c=np.unique(km.labels_))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bBRSSSaZRM08iiSqqz1AzP8YQg2OqmffRSyut2LBRRfWomnkr6aSxXGrmZzRh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\nECbw/wP/xeOruu220wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c7cc198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['scaled_weight'], df['scaled_height'], edgecolor='none', c=df['POS_label'], alpha=0.5)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scaled_prediction  POS\n",
       "0                  C       61\n",
       "                   F       31\n",
       "1                  G       82\n",
       "2                  F      105\n",
       "                   C        5\n",
       "3                  G        1\n",
       "4                  G       92\n",
       "                   F        6\n",
       "                   C        1\n",
       "Name: POS, dtype: int64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('scaled_prediction')['POS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trying to use DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10ccaa518>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N76uqqqiqqhrNjxdCiHNedXU11dXVp33ciM1mUUoVAy/KA1Ahzi1dXR7+/vedtLe7mTIl\ng+uvn8r69XX89rcb8HiCVFbmkpHhYNOmBnbtaqOnx0dOTgKXXjqRrVtb2bOnnVBIs2JFKcXFKTz2\n2Fba2tzY7WZuvXUGeXlJbNrUSE+PlwsvLOAzn5nFQw9t5p136ggEwsybl88dd1RSWZkHwP/5P2tZ\nu/YwSUl27r33YubOzTtp/zdtauAXv1iH0+ln2bJS7rlnCVu2NPPPf+4H4IorJjFrVs5Zv45jZdTX\nM1dKlRAJ5jM/4n0J5kKMgcce28L+/Ucfaa1YUcrPfvY2Hk8k2/PIkV4qK/PYtKmBnh4fSkXmZaem\nxtHd7cVmM+PzhUhIsGK1mmhtjST7hMOanJwEkpPtmEyRikJaa7KyHLS1uamr60FryMtLZOnSYu65\nZwnr19fx61+/a/QlPT2ef/zjlpP2//rrn6K722tsf+1r89m5s90oH2cyKb75zYUkJdlH8rKdM0Z1\nNotS6m/AO8BkpVSdUupzI9GuEGL4BooqD2hvdxvZkaGQRmvo7vYQDuv+LNBIvU2PJ2i8BuDzhfqz\nO4++5vUG8flCxoJa4bCmu9tLIBA2skBDIY3XG8Tp9NPWFp312dd38gzMcDh83D4NDX1RdUDDYX1c\n5uh4NFKzWT6ltc7XWtu11hO01g+PRLtCiOGbMyfX+N5iMTF/fgGVlZHXrFYTqalxzJyZQ3KyHbvd\ngtmsiI+3MGVKBvHxViMDtKgomcmTM7BYTJjNkdBRXp5BSUkqCQmRRB6Hw8rll5eRleXAbjdjsUTu\n2MvLM8jLS2L58tKopJ9Fi4pO2neTycTChUf3cTisXHvtFPLzk4zX8vOToup/jleSASrEOLB3bwcd\nHW4mTkwjJyeRYDDMc8/txOn0s2JFKe3tHrq7Peza1UZjYx+TJmUwZ04ugUCIVav2kJpqp6qqhIwM\nB9XVB3nrrcNMnZrBihVllJSk8v77jTQ3O1m2rISJE9OoqWli69YWgsEw06ZlsWBBAXZ75JfC4cPd\nvPbaAdLS4rjhhqmYTCe/pwyHw6xatZuuLi+XXTaR4uJUfL4gW7e2oJRi1qwcY6mAWCQ1QIUQIgbI\nqolCCDGOSDAXQogYIMFcCCFigARzIYSIAVIDVIgx5PMFefHFvTQ09FJUlMLHPjYZq/XEMzNee20/\nf/3rh3R1eSgrS2PWrFw6OtzU1DSRlGTnO99ZzORCO3WLFhFoaMCcmkrB//4v4ZLJPP/8btrb3Uye\nHJmlcvcPm/mf11Pwe9qxBvYyZ3I7n/zkjP5ph4qVKyfx0kt7+bd/MxMOxwG9XH65h5tvTmPr1hbq\n63spK0vjRz+qora2g298Y3V/jdHIeuN793bS0uIkLs5KYWEiaWkOUlPjyM9PIj8/iWuumUxubgI/\n/enbbN7cTE5OAj/84VLy85Nxufw8//we2tpclJdnsHLlJEymM6t/Op7IbBYhxtArr+xj48YGY3vx\n4iIuv7zsuP1aW53cdts/cLsDNDT0YTIp5s3L4/33mygsTMZkilT5+XX7H/Bs2GAcZy0sZNNPVrFn\nT4fxWmenjz8+v5igpwWCvYDGHviAjGQnN944lfR0B4cPd/Pkk6n4fFmAB3ACQZYta6C52Ul2dgIA\nl146kVde2defyBOmrc1Nbm4CLS1uQqEwVquJUEhTVpaOyxWguDiFJUsmkJoaR3p6PI8+usXoV0VF\nNr///VU8/fR2du1qN15fuXJSVOWl8UZmswhxHujs9Jx0e0Bzs4tAIGzU4wyHNa2tbsJhbWRDdnZ6\nCDQ1RR0X6umJalNrTf0RL2GtQAcBDShC2kEgEDJqYnZ0eAgEBtLjQ/1/RgorBwIho72mJucx7YPT\nGTT6NJBh6nT60Vrj94cIBEI4nX7q63ui+tra6gKgq8sb9fpHXRMRTYK5EGPo2HqW06aduL7l5MkZ\nZGcnYLOZMZtNOBxWpk3LJCHBitkcuWmbOTObxJUrjRqdAPbZs6Pqh5pMimVVucTb/GCKA2UGHSDO\n3El6ejy5uZHMygsuyCMjo5NIsLcDCpPJx4QJKVEZnEuWFDF7dm5/2yasVjO5uQ6jlqjNFskCzcpy\nEBdnIS0tjrg4C6WlqaxYMTFq+OTCC/OPuyZKwZQpGad/YcchGWYRYozt2tVGQ0MfRUXJTJny0cWK\n29vdPPHEVjyeIJMnp5OVlUBioo3XXz9AWlo8t98+C5vNQsv3v4/7jTewTZ9OwX//N1prtmxpob3d\nTXl5OgUFybyyuoEf/i6e9pYu8pL3cMUyB7fdNpPmZqeR8r93bwe33bafPXsSSE4O86c/xTFpUhqN\njX1s3drCrFk5XHVVOX5/iJ///G2OHOnlyisn0dbmprGxj8OHu7FYzMybl4fXG6SwMIXERBtJSTYu\nvLAAq9XM+vV1rF9fT1FRMrfcUmFkg27Z0kxbm5uysjRKS9NG60dxTpIMUCGEiAEyZi6EEOOIBHMh\nhIgBEsyFECIGSDAXQogYIBmgQpwjPJ4A//M/u436mNdfP5XFi48WZqiv7+GNNw4CsGxZCcXFqTz9\n9HZeeaUWl8vPBRfkU1qaQm1tF7t2tREKaRYvnkB5eTqXXTbRKCgB8P+th++9AIEwzHe0UNa4kY0L\nHPR+ogBripU7SeEesvjrX2tYu/YwHk+Q+fPz6ehw849/7KatzUVWVgK/+MVyLr10Eq++WktPj48Z\nM7JZuLCQzZub+MMf3icQCLNsWTGhkMZsNnHppRNpbnZSU9NEYqKN5GQ7DQ29ZGY6uPLKcuLiJCSd\nKZnNIsQ54u9/38GTT243kmSmTs3krrsupKIiG48nwIMPbsTrjZR7s9vNLFhQwE9+shaXK0Brq4uk\nJBslJans29eJ3W6mpycyL3z58lKWLi1m+fJS47PivgnBMOhgEL1vB/mZBwj9ZxnKrDBn2TGZTfz4\nnyH++2fv0tfno6PDg91upr3dTXe3F6UUSkFJSSo331wRtQTBtddO5oc/fBOXK0AwGKapqY+PfWwK\nqalxeL1BrNZIpaKmpj4aG51ccEGkoHNFRRaf+ETFKF7x84PMZhHiPNPS4sLlOlqv0+n009ISyYrs\n6fEZgRwi9Ti3bWtBa/D7IxmZfX1+Ojs9+P0h47WeHm9/207j2J1NkUAOgN+L1hpnWRKqP/mIoCYM\nVLdHlgDw+yM79/ZG92Hgtbq66EzOvXs7jJqcgUCIUEjT1RX5BdXe7sbni/TN5QpEne/AuYozI8Fc\niHPExIlppKXFG9vp6fFMnJhmfJ+aGme8l5wcKeNmsZiMoYmMDAcFBcnEx1uIj49kaebkJBptD5ie\nB/EDSZz2eJTJROrmTrQvFMketZqwALeW5WIyReqBAmRnJ5CUZDMyTJVS5OYmMmtWjtG2UjBvXr6x\ndovNZiYuzkJWVmS7tPRovdC0tDjS0o6e0+A+itMnwyxCnCPCYc26dYdZt66OlJQ4Lr+8jPLyo6ns\n3d1e3nmnHq01ixcXkZYWz7vv1vPSS/sIBsNUVuaQl5dES4uLzZubMZsVc+fmUVycyty5eVGf9f5h\n+NSj4AnAJyf3kri/hpp8qL01D3uilZ+RyUqSWbPmIG+8cZBwOMzcuXm4XAGeemobtbVdlJWl8ctf\nXsaMGdmsX19HT4+PioosysrSaW528pe/1OD3h7jqqkm0t3swmxVLlkygs9PDli0tJCbayMiI59Ch\nbjIzHSxaVCSrI56AZIAKIUQMkDFzIYQYRySYCyFEDJBgLoQQMUCCuRBCxABJtxLiDPh8Qd544yDd\n3V4qKrKMAg3HOnSomw0bjmC3m1m+vJSUlLjj9gmFwqxde5imJiclJaksWlSIxxPkjTcO4HT6qazM\nO66IxYA9e9qpqWkiIcHGihWlJCTYjmv30KFuNm5swGRSLFxYyO23z+bIkV4eeOAd9u7toKgohWAw\nhMsVQClwOKxUVGRTWppGTk4CBw92k5JiZ8WKiVEZmoFAiDffPERjYx/d3V6ysxOYOTObmTNzTtRV\nDhzo4r33GrDbzaxYMZHkZDuPP76Fd99tID8/kW9+cyEOh+2Ex4pTk2AuxBl4/vk97NzZBkSSZBIS\nbEyalB61T2enh8cf32qUemto6OOrX50fVQkIYM2ag6xfX2+0ZbGY2LmzjUOHuo3X7rxzLoWFyVHH\nNTb28fTTO4wSbW1tLu68c67x/ptvHmLdujreeOMA9fW9pKfHs3NnG0lJNp58cjs1NU34/ZGAnJxs\nx+sNoJQiMdHGunV1rFgxkUOHupk/vwCIJAjdeutMo/1XXqmlpqaJbdta6ehwM21aJnv3duBwWCkr\ni74W7e1u/va3bca1aGzso7Awmb/85UMAduxoxen08/OfX3q6PwrRb0SGWZRSK5VSu5VSe5VS3xuJ\nNoU4lx1bv/LYbYDmZqcRvCAS0Dye4HH7HTnSe1xbg9vTGhoaeo89jMbGPiOQD7QzePrvQLsDywP4\nfJHP3rSpkaamPoLBSE3RcFjj8QQIhTTBYJhAIIzXG6K52Wmk5Ef6Fd2HgfYH6oYO/Hns+ZzoWrS1\nufnww+aofWpru447TgzdsIO5UsoE/D/gCqACuFUpNXW47QpxLisqSjnpNkBubiIWy9F/YpmZDiOb\ncrBj77iLilKi2lMKCgqSjz2MgoKkqCSbwsLkqLv+gXbT0yNZpXZ75LPnz88nLy8Ji8WExWLqz/KM\n1BK1WExYrSbi4szk5iaSkGA1zqGo6Nh+RraTk+1Rf57oWuTlRV+LrCwHlZXRQ1Pl5enHHiZOw7CT\nhpRSC4Efaa2v7N++B9Ba618es58kDYmY4fMFWbNmYMw8OyqlfbDIePURbLZTj5k3N0fGzBcuPDpm\n7nIFmDMn9yPHzPfu7aCmpgmHw3rcmHk4rFm79jAHD0bGqs3myJj5pz99dMx8374OJkxIIRDQuN1+\nlIqk78+YkU1xcSq5uYkcPNhFSkocy5eXRo2ZB4Nh3nzzIE1NfXR1RcbMZ8z46DHzgX7Y7RaWLy81\nxsw3bmwgPz+Jb3xjgYyZn8CoZYAqpW4ErtBaf6F/+9PAhVrrrx+znwRzIYQ4TUMN5qP6APT+++83\nvq+qqqKqqmo0P14IIc551dXVVFdXn/ZxIzXMcr/WemX/tgyzCCHECBnNtVk2AZOUUsVKKRvwSeCF\nEWhXCCHEEA17mEVrHVJKfQ34J5FfDn/VWu8ads+EEEIMmSyBK8QZCAbDrF9fR3e3l+nTs45bd3z9\n+jogUnBhoIzbxRcX43BYj2srHA7z8MObqa3tpLIyj5tvrsDp9PPHP26iq8vLVVeVs2TJBF5phe/s\nixzzy3K4Ohvefbeexx/fitPp58YbpzNhQgrV1YfweAJcckkJCxcW8PbbdTzwwLu43X6uv34an//8\nXF5//QDvv99IYWEyV15ZTn5+0gnP8403DvDGGwfJzk7gC1+YK7NNxoCsZy7EWbRq1S62bGkBIvPA\n/+Vf5lBSkorfH+L3v3/PKPO2bVsLlZV5WCwm8vIS+eIX5x3X1oMPbmDVqt3G9pe/PI+33jpsZJia\nzYrv/nwln+jLob+CG1YTPJPays/+7SUjmSchwUp+fhKBQGSnwsJkZs/O4bHHthql3RwOK1dcUUZ7\nu5u+Pj8mk2LRokK+/e3FUVWOADZuPML3v/+GkZh0wQX5PPDA5SN4FcVQyHrmQpxFBw4czVbUOjKH\nGiJZnj09kUzIvj4fLlcAjydSD7OpyYnbHTiurYFfCgPef7+RPXvaje1QSPPkhhYjkAMEwvD41h6c\nzqM1NNvb3TQ1Ha312dXlYdu2VtrajtbWDARCvP9+E319kePCYU1bm/uEWZvvvdcQlWG6a1fbSa6I\nGGsSzIU4AwO1NQfk5ka2U1PjsNsjleodDmtUjc6UFPsJM0CLi6MzJidOTCMvL3rYY/nUNMyD7s1M\nCi6d6DCyOgESE+1RNTUTE21MmJBiZGYCWCwmJk5MjUr+SUqyGTU7Bzt2rZljM1XFuUWGWYQ4Ay6X\nn9Wra40x80WLioz36ut7WLPmIBBJbT90qBu73cwVV0wiM9NxXFtut59f/Wo9Bw92U1GRzd13L6Kx\nsY8HHniHnh4fV1xRxq23zuQ/D8ADdYCGuybAD8rgmWd28OijW/B4AtxwwzRKS1NYs+YwPl+A5ctL\nufzySTx1UgqzAAAYfElEQVT33E4efHAjXm+QSy8t5T/+Yzl///sONm1qpKAgiU98ooLp07NOeJ4P\nP/whb711mPT0eL773YuMX1pi9MiYuRBCxAAZMxdCiHFEgrkQQsQACeZCCBEDJJgLIUQMGDdl45xO\nP5s2NaCU4sILC06YiSfEcPh8QTZubCAYDDN3bh6pqXF0dnqorj5IXV0vc+bkctFFRVitZg4e7KK2\ntpOsrATmzMnlued2sm9fJ3Pn5pKe7qCxsYdXXqmlocHJxRdP4LOfnUNHVg+PsJ6gP8zMDyZSEsgm\nMdHG668fIDU1jttvnx015XBAOBzm6ad3cPhwD0uWTGDJkglR77vdAd57rwGtNfPnF5CYKFme56Nx\nMZvF7w/xpz+9b5TPyspy8MUvzouqfCLEcGit+ctfamho6AMic7c/85nZ/OlP77N27WGCwTApKXZu\nvrmCBQsKeeKJrQz8c3C5/Gza1AhAR4ebGTOyqalpor6+16gGdMNtkwk/0EnYGibkA+2DxJ9ms+7l\nBjIz41FKMXt2Dg8+eOVxfXvggXd48cW9QCRb9cc/rmLp0hIgsizBn//8Pm1tbiBSlehLX5qHzWY+\ny1dMDJXMZhmktdVlBHKI1B/s6HCPYY9ErOnr8xuBfGD7gw8aaWzsM2pf9vT42LmzjZ072xh8XzNQ\nzBnA7Q5SV9dDa6uLcFgTCkVqdG7vbsLvC6HDABpl19Snt+Ny+QmFIo1t29ZKODwoTbTfxo0Nxvda\nw9q1dcZ2R4fbCOQQqRfa2upCnH/GRTBPSbFH3YVbrSaSkuwnOUKI0xMfb4nK7jSZFEVFKVGvWa0m\nMjIcZGVFJw7l5CRE7ZOUZCc+3opSGDU+03CgLAr67890GJL74jGZlLFPeno8JtPx/6QHtw+R2qED\nkpLsWK1Hj7FYTFEZo+L8MS6GWQD27GnnjTcOohRcdlnZcanKQgxXXV0Pr7yyj0AgzNKlxcyalUNN\nTRN/+9s2jhzp5YIL8vjMZ2aTnZ3A6tW1/WPmDpYuLebXv36HuroeSktTmTYti+3bW3n55Vrc7gDl\n5en8/Ocr2LpgPxvYT8ivcXyQyKwtZXR1efjggyYSE2185zuLqajIPq5fzc1OfvKTtTQ391FZmce9\n9y6JCvq1tZ289tp+tIYVK0qZMuXE9UbF2JAMUCGEiAEyZi6EEOOIBHMhhIgBEsyFECIGSDAXQogY\nMG4yQIU424LBMFu2NBMIhJk1K+ejs4zbH6H78N9o6CjDVvoTysszWHPffWxe8z7JF1zC1fd+jbwM\nOzz/CXA2w4V3Q8UtNDc7ue++NYTDmu9+dzHd3T527GhlzZpD5OYm8K1vLeTQoV7SNz7D1EOvwLT5\n8KWfA7B3bwft7W4mTkyTNcljlMxmEWIEaK15/PGt7N8fKR+Xnh7PF794QVQlIACaf4/n4Pdo6bAD\nip1HKtnxbDZta1fTHk4DFLZF1/Ljlc+TYzoUOcZkwnnpI8z6RCudnR601pjNJq65ppznntuF1pH5\n4ampcdwxuwu2v8uy1ENckloPi65k/dUP8tprB4DIfp/97BypGnQekdksQoyivj6/Ecghkkk5UGg5\nSufDuL1mBrJ/ijN3s++97fTpgcQejWvHJro6j2aTEg6z54U/G1nM4bDG7Q7w9tuHCYW0kSna2uoi\nfHAnaM0WZ07k2C3ro2qMBoNhtm9vHclTF+cICeZCjIC4OEvUeiZKRdZnOY4lC7PpaMq9zx+HPd6G\nmdDRY+MTsZijb8RSc/NQSvW3rVAK0tOjMzstFhMmezwASeZIUWniE47rh2R4xiYJ5kKMAJvNzE03\nTSclxY7DYWXlyknHFX0GoPQpklJzibOHcXoS2ef6Jl96/Dfkp4HdFCSckMGKH3yXkqvvArMVTCZI\nLaXsy0/yr/9aic1mJi7OwrXXTuHmm6dTWppKfLyFlJQ47rijEvuVt5KXZuLa7P2QkAI/fJRrrplM\nQUESdruZmTOzWbCgYPQvkDjrZMxcCCHOYTJmLoQQ44gEcyGEiAESzIUQIgYMK5grpW5SSm1XSoWU\nUnNHqlNCCCFOz3AzQLcBNwB/HoG+CHHOa2rqo6XFRVFRMrt3t9PQ0MfixUV4PAE8niDl5enEx1sJ\nBsPs3t2OO66Fzon/RPscZO69Bqu2Ek54A4/pf6l5u4y+xsu46qpycnISaWlx0T/7kOLiVF56aS9b\ntzZTVVXCsmWl2Gxmdu9uB6CkJJWdO9vYvbuN5mYnKSlxXHppGRMnptHR4aa+vpfs7ATy85NOcjYi\nlozIbBal1JvAt7TWNSfZR2aziPPajh2tPPvsTrSGDz9soqXFRVycBafTz+LFhaSnO0hPj+eOO+bw\n9NM7qHcdIu9LLxDWmroaO90HEmh+MZXFV73JjpoMtryXQ0+HAxWcwPz5+SQn26mt7WT27BzWravj\n4MFugsEwNpuJz3/+AkpK0mho6CUUCrN/fxfNzX1s3tyCzxckIcHGjBnZfOlL89i3r4NAIIxS8PGP\nT2PmzJyxvnRiGGQ2ixAjbNOmRqN255497TidfrTWtLe72bu3E4hkfr7zTj319b2kLt2CsoRxtpkI\neBWOPDdpE/ax7p+FHDmYTDikcCR5aWtzsWdPOw0NvYTDmsZGJ/v3d+LxBADw+8OsWXOQDz6IFH3u\n6vKyd28H9fW9+P0hQiGNzxfkwIEuXnxxD4FAJClJa4xC0SL2nXKYRSn1GjD4V7sCNPADrfWLp/Nh\n999/v/F9VVUVVVVVp3O4EGMqLm5wPU+zkZFpMins9qPZnykpcQCEXJE/Tf2H6ZAiFDARF+/H54vs\nr8ORGp42m9moU2u1mrBYTEZQBnA4rEaGqdVqwmw2YbEoY1hGKXXC2raDa5CK80N1dTXV1dWnfZwM\nswgxRF1dHp54Yhvt7W5CoTA1NU24XAEmTEhm8uRMQqEw8+blc/XVk1m3ro41aw6S+dVnsKa6aNpj\npXlNKd0b8rnp639g2/upvPqPEuprc5lcVsbSpcX09fk5cqSXsrI0uru9vPzyPrq7feTmJvCLX6wg\nLy+ZV1+tBSJFyjdsOML69fU4nX4SE22sXDmJu+9ezPr1ddTV9ZCZ6eBTn5pJenr8GF85MRyjWgO0\nP5h/W2v9wUn2kWAuYkIwGDbuov3+IDabBa0jC16ZzUdHLsNhjVLgUi5s2DCHLJhMKrIwlrmVsDdS\nOHngjn+g3cHtu90+7Har0W44HPk3ZDIpQqEwWkcW2TKZFFar5YR9FOe3UQnmSqnrgf8CMoFuYLPW\n+sqP2FeCuRBCnKZRvTMfCgnmQghx+mQ2ixBCjCMSzIUQIgbIvCUhztDatYf48MMmLrggn/nzC7Db\nLfz5z5toaHBy883TmD49h95eH3/9aw0ZGQ5uvXUG9fW9tLT08fbbdcyZk8vKleVAZKZMe7ub3NxE\nY3rh5s1NdHR4WLCgkMTEExS6+AhOp5+mpj4yMhwyk2UckTFzIc7AT3+6lj/8YRNudwCbzcwXvjCX\nN988xObNzYRCmoQEKz/60SX87ncb6Oz0orUmNzeRiy4q4umnd/TPCzfzr/9ayZe+NI8nn9xOMBgm\nLs7CZz87h1WrdvH00zsAyMpK4M9/vpr0dMcp+9Xa6uLhhz/E4wlisZi45ZYKysszzvblEGeRjJkL\ncRY9/fT2QRmaIVat2m0EcgCXK8D//b8baW+P1O0MhTSHD/ewZs0hQiFNMBiZVvjUU9tZv76eYDCS\nIOT1Btmw4QirVu02PqutzcVLL+0bUr82bjyCxxMEItMT162rG7FzFuc2CeZCnAGr9WhRZojMFR/I\nCB0wOCsUInVBB9cJBbBYzMfNB7daTVit0a8Nzj49db8+elvELgnmQpyB731vMSkpkbHt5GQ7d945\nlxtvnGYE4ZycBH7968upqMgCIsWWFy8u4qqrynE4LEbA/v73L+KyyyYaRZYzMx0sXVrMV796odHW\n9OlZXH/9lCH1a8mSCWRnRwo9JyXZuOyyiSN63uLcJWPmQpyhnh4PdXU9FBUlk5ISj1KKQ4e6aW11\nUlGRTUJC5KHl1q3NJCfHUVycgtPpx2yGHTvaKS/PIDW1fx2XUBiXK0Biog2TKXKH73T66e31kpub\niMk09PuucFjjdPpJSLBGZaSK85MkDQkhRAyQB6BCCDGOSDAXQogYIMFcCCFigARzIY4RDIZpbOyj\nt9cHgM8XpKGhF7c7Mq/c5fLT0NBLR4ebxsY+AoHQCdvRWnPkSDd//vMmnn12B11dno/8zLq6Hl5+\neR9tbU4Aent9NDb2GfPPB+zY0cqOHa3GdigUpqmpj+5u77DOWZz/JJ1fiEF8viAPP7yZ5mYnJpNi\n+fISNm5soK/PT1ychYsvnsBbbx2moaGXgwe7mTUrh6KiZO64o5L4eKvRjtaaxx7byre+9aoRaMvL\nM3jooetYuLAw6jNfemkvd9/9Kn5/CIfDyn33Xcz+/V2EQpGs0c99bg52u4V///c3Wbv2MAAXXTSB\n+++/hEcf3UJ9fS9KwdVXT2bevPzRu1jinCJ35kIMsnlzM83NkbvjcFjz0EOb6evzA5HszIcf/hC/\nP8SBA1243QGOHOmlrc3NBx80RbVz+HAP//3fm+np8aF1pB7ngQNdrFq10ygwMeA3v3kHvz9yd+92\nB/jlL9cbmaTNzU4+/LCZPXvajUAOsH59Hc8/v5v6+l4g0v5AFSIxPkkwF2IYjtbg/Oj3Tv2aOsU2\nxtzzwY597djjxPgiwVyIQebMySUvLxEAs1lx552VRnZmXJyFO++ci91upqwsnYQEK4WFyWRnJzB3\nbl5UO8XFKXzuc5WkpsYZwXjSpHQ+/vGK44Lwt7612Ej9T0y0ce+9SzCbI/vk5SUyZ04u5eUZLFtW\nYhxz8cUTuPbaqRQXpwCR9q+4ouxsXBJxnpCkISGOEQqFaWtzk5BgJSnJjt8foqPDTWpqHPHxVtzu\nAD09Xux2C35/iMxMxwnrbWqtaWrq5bXXDpCcHMeyZaVGxuexGhp62LmznQsuyCc9PZ6+Ph8uV4Cs\nLEdUFueePe2Ew5pp0yLLBITDmtZWF/HxFlJSTty2OL9JBqgQQsQAyQAVQohxRIK5EELEAAnmQggR\nAySYCwH09fmMDM+ham52sm5dHcFg8ITvO51+XC4/u3a10dDQg9aa7m4PTU19tLa6jLnlp8Pl8uN0\n+k/7OBH7JANUjHsvvLCHmpomlIIrrph0XIbmifzudxv4yU/eIhTSZGY6WLfuDnJzE433V6+u5Z13\n6njmmZ10d3uJi7Mwe3YOoZCmtraTtLQ4Lr64mK9+dT7FxalD6uebbx7krbciiUNLlkzg0kul8IQ4\nSu7Mxbh2+HA3NTWR7M2BLEqv98R32oP9+tfvGFma7e1uvv3tfxrvNTc72bDhCNu3t9Hc7MTrDeLx\nBFiz5iCHD3fj9QZpbXWxfXsrq1cPLWuzq8tjBHKAdevqaG93n86pihgnwVyMa8cuZKV1ZJ75qRy7\nj9d7dIhmoM1jF+DSGiOVP/I5+rjPH2o/P+o1MX5JMBfjWklJKqWlR4c5LrywwCj3djI331xhpObH\nx1v493+/xHivoCCJyZMzmDkzh+RkOzabGbvdwvTpmeTmJmE2K9LS4pg4MZVLLikZUj+zshKYMSPb\n2J4+PYucnIShnaQYFyRpSIx74bDm8OFurFYzhYXJQz5u9ep97NjRxo03TqekJHrcW2vN4cM9BAJB\namqaSEqKY8WKUvbv76S93U18vJWJE9PIyHAM+fO01tTX96K1ZsKEFFmLZZwYlQxQpdSvgI8BPmA/\n8Dmtde9H7CvBXAghTtNoZYD+E6jQWs8B9gHfH2Z7QgghzsCwgrnW+nWt9cBTmA3Aqed0CSGEGHEj\n+QD0DuCVEWxPCCHEEJ0yaUgp9RqQM/glQAM/0Fq/2L/PD4CA1vpvZ6WXQoyyQCCExWI66UNGrTWB\nQAilFFaredif6feHsNmG344Yn04ZzLXWl53sfaXUZ4GrgOWnauv+++83vq+qqqKqqupUhwgxqsJh\nzbPP7mTnzjYcDiu33FJxwgzNI0d6+eMfN/Heew0kJ9u56abp3HLLjBNWBDqVri4Pjz++lY4ODwUF\nSdx22ywcDuupDxQxqbq6murq6tM+brizWVYCDwBLtdYdp9hXZrOIc15NTRMvvLDH2E5Pj+frX19w\n3H5//OMmXnhhDx5PJFu0vDydr3xlPpWVecfteypPP72dXbvaje2FCwtZuXLSGfRexKLRms3yX0Ai\n8JpSqkYp9YdhtifEmDo2lf+jUvu93iCBwNEMzEAgPKRlAIbymR7P6S34JQQMfzZLuda6WGs9t//r\nKyPVMSHGwowZ2SQlHc0AXbToxBO0Fi0qoqgokmBktZopK0ujoiL7hPueyoUXFhjDM1ariXnz8s+o\nHTG+SQaoEMdwOv0cPNhFcrL9pCsa1tX1sHNnG6mpdmbMyCEx8dTLAHyUlhYnLS0uCgqSTisrVMQ+\nqQEqhBAxQGqACiHEOCLBXAghYoAEcyGEiAESzIUQIgZIMBdCiBggwVwIIWKABHMhhIgBEsyFECIG\nSDAXQogYIMFcCCFigARzIYSIARLMhRAiBkgwF0KIGCDBXAghYsApa4AKIYbm8OFuXn55H4FAmEsu\nKWb27Fw++KCR9evrsdnMXHPNZAoLk8e6myJGyXrmQoyAQCDEAw+8a5SAUwpuvHEazz23i4G/9g6H\nlW9/e/EZFX0W45esZy7EKPJ4glG1PLWG+vo+Bt+/uN0BfL4zqxMqxKlIMBdiBCQl2YyaoADJyXbm\nzcsjIcFqvFZSkkp8vPVEhwsxbDLMIsQI8fmCvP9+I4FAmLlz80hOttPV5WHz5mbsdgvz5+djtZrH\nupviPCM1QIUQIgbImLkQQowjEsyFECIGSDAXQogYIMFcCCFigARzIYSIARLMhRAiBgwrmCul/kMp\ntUUptVkp9bpSqnCkOiaEEGLohntn/iut9Wyt9RzgeeD+4Xfp/FRdXT3WXTirYvn8YvncQM5vvBhW\nMNdaOwdtJgDtw+vO+SvW/0LF8vnF8rmBnN94MewlcJVSPwVuB9zAgmH3SAghxGk75Z25Uuo1pdTW\nQV/b+v/8GIDW+j6t9QTgYeB3Z7vDQgghjjdia7MopYqAl7XWMz/ifVmYRQghzsBQ1mYZ1jCLUmqS\n1rq2f/N6YPNwOiOEEOLMDOvOXCn1LDAZCAEHgC9rrVtHqG9CCCGGaNSWwBVCCHH2jGoGqFJqvlLq\nPaXUh/1/zhvNzz/blFJ3KaV29T8k/sVY9+dsUEp9SykVVkqlj3VfRpJS6lf9P7vNSqnnlFIxUXlZ\nKbVSKbVbKbVXKfW9se7PSFJKFSql1iildvT/m/v6WPdppCmlTEqpGqXUC6fad7TT+X8F3Ke1rgR+\nBPznKH/+WaOUqgI+Bszsfwj867Ht0cjrz/C9DDg81n05C/4JVPQnwO0Dvj/G/Rk2pZQJ+H/AFUAF\ncKtSaurY9mpEBYG7tdYVwCLgqzF2fgDfAHYOZcfRDuZNQEr/96lAwyh//tn0ZeAXWusggNY6FhOo\nfgt8Z6w7cTZorV/XWof7NzcAsbA0xYXAPq31Ya11AHgKuG6M+zRitNbNWuvN/d87gV1Awdj2auT0\n3zxdBfxlKPuPdjC/B/iNUqqOyF36eX/3M8hkYKlSaoNS6s0YHEK6FqjXWm8b676MgjuAV8a6EyOg\nAKgftH2EGAp2gymlSoA5wMax7cmIGrh5GtKDzWFngB5LKfUakDP4pf7O3AfcBdyltf4fpdRNwENE\n/tt+XjjFuVmANK31QqXUfOAZYOLo9/LMneL87iX6Z3XeTTU9yfn9QGv9Yv8+PwACWuu/jUEXxRlQ\nSiUCzwLfOGaJkfOWUupqoEVrvbl/CPfcKegMoJTq1VonD9ru0VqnnOyY84VS6mXgl1rrt/q3a4EF\nWuuOse3Z8CmlZgCvE1myQREZgmgALoylqahKqc8CnweWa619Y9ydYVNKLQTu11qv7N++B9Ba61+O\nbc9GjlLKArwEvKK1fnCs+zNSlFI/Az5N5LlAPJAE/ENrfftHHTPawyz7lFKXACilVgB7R/nzz6b/\nAZYDKKUmA9ZYCOQAWuvtWutcrfVErXUpkf+uV8ZYIF9J5L+018ZCIO+3CZiklCpWStmATwKnnBVx\nnnkI2BlLgRxAa32v1nqC1noikZ/bmpMFcjgLwyyn8EXg9/1/sbzAF0b588+mh4GHlFLbAB+Rxcdi\nleY8HGY5hf8CbMBrSimADVrrr4xtl4ZHax1SSn2NyEwdE/BXrfWuMe7WiFFKXQTcBmxTSn1I5O/l\nvVrr1WPbs7EhSUNCCBEDpGycEELEAAnmQggRAySYCyFEDJBgLoQQMUCCuRBCxAAJ5kIIEQMkmAsh\nRAyQYC6EEDHg/wfrZp3bnLz2egAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c9ff3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "db = DBSCAN()\n",
    "df['db_category'] = db.fit_predict(df[[\"Height\", \"Weight\"]])\n",
    "plt.scatter(df['scaled_weight'], df['scaled_height'], edgecolor='none', c=df['db_category'], alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6837098610421475"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similarity measure between two clusterings by considering \n",
    "# all pairs of samples and counting pairs that are assigned\n",
    "# in the same or different clusters in the predicted and true clusterings\n",
    "\n",
    "from sklearn import metrics\n",
    "metrics.adjusted_rand_score(df['POS_label'], df['scaled_prediction'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.64644401811332386"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mutual Information is a function that measures the agreement predicted plus actual\n",
    "from sklearn import metrics\n",
    "metrics.adjusted_mutual_info_score(df['POS_label'], df['scaled_prediction'])  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.67802428868913123"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# homogeneity: each cluster contains only members of a single class.\n",
    "\n",
    "from sklearn import metrics\n",
    "metrics.homogeneity_score(df['POS_label'], df['scaled_prediction'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.64816747653318207"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# completeness: all members of a given class are assigned to the same cluster.\n",
    "\n",
    "from sklearn import metrics\n",
    "metrics.completeness_score(df['POS_label'], df['scaled_prediction'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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